Paper
23 August 2023 A prediction model of urban fire with grey Markov model
Ziwen Zheng, Slam Nady, Jingrong Wang, Mingtong Zhang
Author Affiliations +
Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127840R (2023) https://doi.org/10.1117/12.2691815
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China
Abstract
In recent years, urban fires have become a frequent topic of hot search, the casualties and economic losses caused by many discussions, urban fires are getting more and more attention. Forecasting the number of fires by computer is beneficial to reduce the losses caused by fires and provide assistance for arranging the deployment of fire police and making decisions as soon as possible. Based on the prognostication of fire accidents using the gray model, a Markov model is introduced to rectify the residual error in the prediction of the gray model, thereby enhancing its predictive accuracy. In this paper, the fire accident data of Beijing from 2015 to 2020 were used for modeling and verification analysis, and the future was predicted to judge the change trend. The experimental results show that the accuracy of the grey prediction model combined with Markov model is higher than that of GM(1, 1).The mean square error ratio is accurate to within 3.8 percentage points, and the optimized model can be more effectively utilized for predicting the incidence of fire accidents.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ziwen Zheng, Slam Nady, Jingrong Wang, and Mingtong Zhang "A prediction model of urban fire with grey Markov model", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127840R (23 August 2023); https://doi.org/10.1117/12.2691815
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KEYWORDS
Fire

Data modeling

Matrices

Neural networks

Error analysis

Inspection

Modeling

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